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In this paper, we propose a pre-processing technique to improve existing string similarity join algorithms using fuzzy clustering. Our approach first identifies groups of related attributes and then, using this information, we apply existing string similarity join algorithms on these attributes. To identify the clustered attributes we use fuzzy techniques. This approach can be applied to the integration...
Multivariate statistical methods for the analysis of large quantities of data have been applied to problem solving in different domains during the last decades. This paper summarizes the main points of the principal components analysis (PCA) method and its robust fuzzy alternatives, and describes a few applications highlighting the practical usefulness of this approach.
Component selection is a crucial problem in component based software engineering. Component based software engineering (CBSE) is concerned with the assembly of pre-existing software components that leads to a software system that responds to client-specific requirements. We are approaching the component selection involving dependencies between components (requirements). We formulate the problem as...
Principal component analysis (PCA) is a favorite tool in environmetrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between...
The problem of a new robust algorithm for estimation of central location has been described in a mathematically simpler way using the fuzzy sets theory. It was compared with ordinary mean estimator and other robust estimators — median, 5% trimmed mean and Huber-, Tukey-, Hampel-, and Andrews-type M-estimators. The performance of Fuzzy 1-means algorithm (FM) proposed is demonstrated by applying it...
In Bezdek and Harris [J. Math. Anal. Appl. 67 (1979) 490-512] an algorithm (called MiniMax, in short MM algorithm) for the convex decomposition of a fuzzy partition has been proposed. In Part I another decomposition algorithm (called MiniMiniMax, in short MMM algorithm) is considered. A comparative study of these algorithms is done.From this study we may conclude:(i) the MM convex decomposition...
In Bezdek and Harris (1979) an algorithm (called MiniMax, shortly MM algorithm) for the convex decomposition of a fuzzy partition has been proposed. In this paper another decomposition algorithm (called MiniMiniMax, shortly MMM algorithm) is considered. A comparative study of these algorithms is done.From this study we may conclude that(i) the MM convex decomposition sequence is not lexicographically...
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